
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
    from ...onnx.config import PatchingSpec
    from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	Any       			=  {
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""longformer"""
  def __init__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Union[List[int], int] = 512     ,				UpperCamelCase__     :					int = 2     ,				UpperCamelCase__     :					int = 1     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					int = 2     ,				UpperCamelCase__     :					int = 3_0522     ,				UpperCamelCase__     :					int = 768     ,				UpperCamelCase__     :					int = 12     ,				UpperCamelCase__     :					int = 12     ,				UpperCamelCase__     :					int = 3072     ,				UpperCamelCase__     :					str = "gelu"     ,				UpperCamelCase__     :					float = 0.1     ,				UpperCamelCase__     :					float = 0.1     ,				UpperCamelCase__     :					int = 512     ,				UpperCamelCase__     :					int = 2     ,				UpperCamelCase__     :					float = 0.02     ,				UpperCamelCase__     :					float = 1E-12     ,				UpperCamelCase__     :					bool = False     ,				**UpperCamelCase__     :					Optional[int]     ,				)     ->      Any:







    """simple docstring"""






    super().__init__(pad_token_id=UpperCamelCase__     ,				**UpperCamelCase__       )

    __magic_name__								=			attention_window
    __magic_name__								=			sep_token_id
    __magic_name__								=			bos_token_id
    __magic_name__								=			eos_token_id
    __magic_name__								=			vocab_size
    __magic_name__								=			hidden_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			hidden_act
    __magic_name__								=			intermediate_size
    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			type_vocab_size
    __magic_name__								=			initializer_range
    __magic_name__								=			layer_norm_eps
    __magic_name__								=			onnx_export







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Any     ,				UpperCamelCase__     :					"PretrainedConfig"     ,				UpperCamelCase__     :					str = "default"     ,				UpperCamelCase__     :					"List[PatchingSpec]" = None       )     ->      List[str]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
    __magic_name__								=			True
  @property
  def 				_lowercase				(					self     :					Dict       )     ->      Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    if self.task == "multiple-choice":
      __magic_name__								=			{0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      __magic_name__								=			{0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
            ("""global_attention_mask""", dynamic_axis),
        ]       )
  @property
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    __magic_name__								=			super().outputs
    if self.task == "default":
      __magic_name__								=			{0: """batch"""}
    return outputs
  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      float:







    """simple docstring"""






    return 1E-4
  @property
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      int:







    """simple docstring"""






    return max(super().default_onnx_opset     ,				14       )





  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					"PreTrainedTokenizerBase"     ,				UpperCamelCase__     :					int = -1     ,				UpperCamelCase__     :					int = -1     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					Optional[TensorType] = None     ,				)     ->      Mapping[str, Any]:







    """simple docstring"""






    __magic_name__								=			super().generate_dummy_inputs(
        preprocessor=UpperCamelCase__     ,				batch_size=UpperCamelCase__     ,				seq_length=UpperCamelCase__     ,				is_pair=UpperCamelCase__     ,				framework=UpperCamelCase__       )
    import torch

    # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
    # makes the export fail randomly
    __magic_name__								=			torch.zeros_like(inputs["""input_ids"""]       )
    # make every second token global
    __magic_name__								=			1

    return inputs



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

